One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs

Tran, Kim Duc; Khaliq, Qurat-Ul-Ain; Nadi, Adel Ahmadi; Nguyễn, Thị Hiền; Tran, Kim Phuc

doi:10.1016/j.jmapro.2021.07.031

Cited by 18 publications

(11 citation statements)

References 29 publications

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“…Also, standard deviation of run length (SDRL), relative average run length (RARL), performance comparison index (PCI), and extra quadratic loss (EQL) are used to evaluate chart performance. To have a fair judgment, given an identical ARL in IC condition, denoted by ARL 0 , the charts try to reach the lower ARL value in OC condition, denoted by ARL 1 3–8 …”

Section: Introductionmentioning

confidence: 99%

Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation

Nadi

Yeganeh

Shadman

2023

Quality & Reliability Eng

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The current study investigates the effect of within-profile and between-profile autocorrelations on the performance of four monitoring methods of simple linear profiles in Phase II. To this end, a general correlation model between error terms is considered such that the correlation structure of within-profiles errors and the error terms between consecutive profiles follow an autoregressive (AR) times series model of order one. Extensive simulations have been done to assess the effect of both autocorrelation types and the profile size on the estimations of the model parameters as well as the performance of control charts. The performance of the monitoring schemes is investigated and compared with respect to the average run length (ARL) metric. The simulation study results show that the autocorrelation within and between profiles has a negative impact on the monitoring technique's performance. It reduces the control chart's ability to detect process shifts compared to no or weak autocorrelation cases. Moreover, the performance of all the methods is improved by increasing the profile size. An illustrative example is also provided to demonstrate the use of the proposed methods for monitoring the stability of profiles in the chemical industry.

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Section: Introductionmentioning

confidence: 99%

Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation

Nadi

Yeganeh

Shadman

2023

Quality & Reliability Eng

Self Cite

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“…However, these machines suffer from low power factor and torque density as compared to PMVM. The statorconnected DC-VRM is also free from the PMs [9]- [12]. Because of its high reliability, this machine is gaining more attention due to which it is being used in high-speed applications.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Single Inverter-Fed Brushless Wound Rotor Vernier Machine

et al. 2022

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“…evaluated the effects of measurement errors on the EWMA scheme to monitor the ratio of two normal variables. In another new development, the one‐sided Shewhart charts for monitoring the ratio of two normal variables in short production runs were investigated by Tran et al 16 …”

Section: Introductionmentioning

confidence: 99%

“…Nguyen and Tran 14 considered two one-sided Shewhart charts to monitor the ratio of two normal variables when measurement errors are present, while Nguyen et al 15 evaluated the effects of measurement errors on the EWMA scheme to monitor the ratio of two normal variables. In another new development, the one-sided Shewhart charts for monitoring the ratio of two normal variables in short production runs were investigated by Tran et al 16 Numerous investigations concerning the effect of measurement errors on the run-length characteristics for univariate and multivariate schemes were suggested in the literature. It is common knowledge that many process monitoring situations contain measurement errors.…”

Section: Introductionmentioning

confidence: 99%

Run sum ratio chart when measurement errors are present

Abubakar

Khoo

Saha

et al. 2022

Quality & Reliability Eng

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As far as literature of quality control is concerned, this is the first article that advocates the run sum ratio scheme with measurement errors, called the RS‐RZ ME chart. The linear covariate error model is employed in designing the RS‐RZ ME chart in detecting increases and decreases in the ratio of two variables from the normal distribution. The average run length and expected average run length values of the RS‐RZ ME chart are obtained using the Markov chain model. A comparison of the RS‐RZ ME scheme with two measurement errors based charts in the literature, namely, the Shewhart ratio and standard run sum ratio charts is conducted. The results indicate the superiority of the RS‐RZ ME chart over the aforesaid existing charts for most of the shift sizes and shift intervals considered. The findings reveal that as the values of the parameters controlling the accuracy error of the measurement system, false(θX,θYfalse)$({{\theta _X},{\theta _Y}} )$ increase, the RS‐RZ ME scheme's efficiency increases. In the same vein, as the values of the parameters controlling the precision of the measurement system, false(ηX,ηYfalse)$( {{\eta _X},{\eta _Y}} )$ decrease, the RS‐RZ ME scheme’ efficiency increases. Furthermore, as the value of the correlation coefficient between variables X and Y increases, the RS‐RZ ME chart's efficiency increases. The application of the RS‐RZ ME scheme is illustrated using data from a food industry.

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One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs

Cited by 18 publications

References 29 publications

Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation

Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation

Design and Analysis of Single Inverter-Fed Brushless Wound Rotor Vernier Machine

Run sum ratio chart when measurement errors are present

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